February 2026: SDPA Operator Performance Optimization in OpenVINO. Implemented unsigned 8-bit KV cache precision for the SDPA operator with NEON/SVE optimizations, delivering a clear throughput uplift in memory-bound large-model inferences and establishing groundwork for larger-scale, batched deployments. Documented trade-offs: while ~27% TPS improvement over OSS master is observed in memory-bound scenarios, small models can incur quantization overhead; default remains F16 for compute-bound use. PR contributions demonstrate collaboration across ARM optimization efforts, with work led by ashwins990 and abhijain1204fujitsu.

Monthly performance summary for 2025-11 focused on stabilizing ARM SVE exp_sve behavior in openvino to ensure cross-arch consistency and SDPA output accuracy. The work targeted critical correctness gaps that affected LLM-generated inputs, delivering a fix that aligns SVE behavior with NEON and eliminates inaccurate results observed with the newer exp_sve implementation.

Month: 2025-10 — Focused on ARM-optimized, low-precision inference improvements in OpenVINO. Delivered INT4 inference support on ARM via the Kleidiai micro-kernel, extended the FullyConnected layer to operate with INT4 weights, and integrated new INT4 matrix-multiplication kernels to boost inference throughput and reduce memory footprint on ARM deployments. This work aligns with performance goals on ARM servers (e.g., AWS Graviton 3E) and establishes groundwork for efficient low-precision inference across workloads like vLLM. Coordination with related PRs (31801) for refactor alignment; co-authored by Aleksandr Voron.

September 2025 monthly summary for openvinotoolkit/openvino. Key features delivered: INT8 Inference Performance Enhancement on ARM with Kleidiai IMM kernel, including kernel integration, quantization/packing optimizations near MatMul, and ARM i8mm support; updated MatMulKleidiAIExecutor to leverage the new kernel. Major bugs fixed: No major bugs reported in this period. Overall impact and accomplishments: Achieved measurable improvements in INT8 inference throughput and latency on ARM devices, strengthening OpenVINO's ARM performance profile and enabling more efficient edge deployments; reduced per-inference cost and extended device compatibility. Technologies/skills demonstrated: ARM SIMD optimization (i8mm), neural network quantization/packing optimizations, kernel integration and executor updates, parallel execution strategies, cross-functional collaboration (PR 31801).

May 2025 — aobolensk/openvino: Focused on ARM64 attention optimizations and ensuring production-readiness. Key features delivered: - Enabled FP16 precision for Paged Attention on AArch64, leveraging Kleidi for optimized matrix multiplication and added SVE FP16 support. Build system updated to include these optimizations. Major bugs fixed: - No major bug fixes recorded for this period in this scope. Overall impact and accomplishments: - Improves inference performance and energy efficiency for attention-heavy models on ARM64 platforms, enabling faster real-time inference and better resource utilization. Strengthens OpenVINO’s ARM64 competitiveness for production workloads. Technologies/skills demonstrated: - AArch64 optimizations, FP16 computations, Kleidi library, SVE, build-system integration, performance-focused debugging.

January 2025: Delivered AArch64 paged attention with SVE support in OpenVINO for the aobolensk/openvino repository. This work updated the build system to include SVE flags and introduced AArch64-specific brgemm kernels and optimizations to improve performance and compatibility on ARM processors. The rollout lays groundwork for scalable, efficient attention workloads on ARM-based inference deployments.